---
_id: '21600'
abstract:
- lang: eng
text: Many problems in science and engineering require an efficient numerical approximation
of integrals or solutions to differential equations. For systems with rapidly
changing dynamics, an equidistant discretization is often inadvisable as it results
in prohibitively large errors or computational effort. To this end, adaptive schemes,
such as solvers based on Runge–Kutta pairs, have been developed which adapt the
step size based on local error estimations at each step. While the classical schemes
apply very generally and are highly efficient on regular systems, they can behave
suboptimally when an inefficient step rejection mechanism is triggered by structurally
complex systems such as chaotic systems. To overcome these issues, we propose
a method to tailor numerical schemes to the problem class at hand. This is achieved
by combining simple, classical quadrature rules or ODE solvers with data-driven
time-stepping controllers. Compared with learning solution operators to ODEs directly,
it generalizes better to unseen initial data as our approach employs classical
numerical schemes as base methods. At the same time it can make use of identified
structures of a problem class and, therefore, outperforms state-of-the-art adaptive
schemes. Several examples demonstrate superior efficiency. Source code is available
at https://github.com/lueckem/quadrature-ML.
author:
- first_name: Michael
full_name: Dellnitz, Michael
last_name: Dellnitz
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Marvin
full_name: Lücke, Marvin
last_name: Lücke
- first_name: Sina
full_name: Ober-Blöbaum, Sina
id: '16494'
last_name: Ober-Blöbaum
- first_name: Christian
full_name: Offen, Christian
id: '85279'
last_name: Offen
orcid: 0000-0002-5940-8057
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
- first_name: Karlson
full_name: Pfannschmidt, Karlson
id: '13472'
last_name: Pfannschmidt
orcid: 0000-0001-9407-7903
citation:
ama: Dellnitz M, Hüllermeier E, Lücke M, et al. Efficient time stepping for numerical
integration using reinforcement learning. SIAM Journal on Scientific Computing.
2023;45(2):A579-A595. doi:10.1137/21M1412682
apa: Dellnitz, M., Hüllermeier, E., Lücke, M., Ober-Blöbaum, S., Offen, C., Peitz,
S., & Pfannschmidt, K. (2023). Efficient time stepping for numerical integration
using reinforcement learning. SIAM Journal on Scientific Computing, 45(2),
A579–A595. https://doi.org/10.1137/21M1412682
bibtex: '@article{Dellnitz_Hüllermeier_Lücke_Ober-Blöbaum_Offen_Peitz_Pfannschmidt_2023,
title={Efficient time stepping for numerical integration using reinforcement
learning}, volume={45}, DOI={10.1137/21M1412682},
number={2}, journal={SIAM Journal on Scientific Computing}, author={Dellnitz,
Michael and Hüllermeier, Eyke and Lücke, Marvin and Ober-Blöbaum, Sina and Offen,
Christian and Peitz, Sebastian and Pfannschmidt, Karlson}, year={2023}, pages={A579–A595}
}'
chicago: 'Dellnitz, Michael, Eyke Hüllermeier, Marvin Lücke, Sina Ober-Blöbaum,
Christian Offen, Sebastian Peitz, and Karlson Pfannschmidt. “Efficient Time Stepping
for Numerical Integration Using Reinforcement Learning.” SIAM Journal on Scientific
Computing 45, no. 2 (2023): A579–95. https://doi.org/10.1137/21M1412682.'
ieee: 'M. Dellnitz et al., “Efficient time stepping for numerical integration
using reinforcement learning,” SIAM Journal on Scientific Computing, vol.
45, no. 2, pp. A579–A595, 2023, doi: 10.1137/21M1412682.'
mla: Dellnitz, Michael, et al. “Efficient Time Stepping for Numerical Integration
Using Reinforcement Learning.” SIAM Journal on Scientific Computing, vol.
45, no. 2, 2023, pp. A579–95, doi:10.1137/21M1412682.
short: M. Dellnitz, E. Hüllermeier, M. Lücke, S. Ober-Blöbaum, C. Offen, S. Peitz,
K. Pfannschmidt, SIAM Journal on Scientific Computing 45 (2023) A579–A595.
date_created: 2021-04-09T07:59:19Z
date_updated: 2023-08-25T09:24:50Z
ddc:
- '510'
department:
- _id: '101'
- _id: '636'
- _id: '355'
- _id: '655'
doi: 10.1137/21M1412682
external_id:
arxiv:
- arXiv:2104.03562
has_accepted_license: '1'
intvolume: ' 45'
issue: '2'
language:
- iso: eng
main_file_link:
- url: https://epubs.siam.org/doi/reader/10.1137/21M1412682
page: A579-A595
publication: SIAM Journal on Scientific Computing
publication_status: published
related_material:
link:
- description: GitHub
relation: software
url: https://github.com/lueckem/quadrature-ML
status: public
title: Efficient time stepping for numerical integration using reinforcement learning
type: journal_article
user_id: '47427'
volume: 45
year: '2023'
...
---
_id: '48778'
author:
- first_name: Maximilian
full_name: Muschalik, Maximilian
last_name: Muschalik
- first_name: Fabian
full_name: Fumagalli, Fabian
id: '93420'
last_name: Fumagalli
- first_name: Rohit
full_name: Jagtani, Rohit
last_name: Jagtani
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
- first_name: Eyke
full_name: Huellermeier, Eyke
id: '48129'
last_name: Huellermeier
citation:
ama: 'Muschalik M, Fumagalli F, Jagtani R, Hammer B, Huellermeier E. iPDP: On Partial
Dependence Plots in Dynamic Modeling Scenarios. In: Proceedings of the World
Conference on Explainable Artificial Intelligence (XAI). ; 2023. doi:10.1007/978-3-031-44064-9_11'
apa: 'Muschalik, M., Fumagalli, F., Jagtani, R., Hammer, B., & Huellermeier,
E. (2023). iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. Proceedings
of the World Conference on Explainable Artificial Intelligence (XAI). https://doi.org/10.1007/978-3-031-44064-9_11'
bibtex: '@inproceedings{Muschalik_Fumagalli_Jagtani_Hammer_Huellermeier_2023, title={iPDP:
On Partial Dependence Plots in Dynamic Modeling Scenarios}, DOI={10.1007/978-3-031-44064-9_11},
booktitle={Proceedings of the World Conference on Explainable Artificial Intelligence
(xAI)}, author={Muschalik, Maximilian and Fumagalli, Fabian and Jagtani, Rohit
and Hammer, Barbara and Huellermeier, Eyke}, year={2023} }'
chicago: 'Muschalik, Maximilian, Fabian Fumagalli, Rohit Jagtani, Barbara Hammer,
and Eyke Huellermeier. “IPDP: On Partial Dependence Plots in Dynamic Modeling
Scenarios.” In Proceedings of the World Conference on Explainable Artificial
Intelligence (XAI), 2023. https://doi.org/10.1007/978-3-031-44064-9_11.'
ieee: 'M. Muschalik, F. Fumagalli, R. Jagtani, B. Hammer, and E. Huellermeier, “iPDP:
On Partial Dependence Plots in Dynamic Modeling Scenarios,” 2023, doi: 10.1007/978-3-031-44064-9_11.'
mla: 'Muschalik, Maximilian, et al. “IPDP: On Partial Dependence Plots in Dynamic
Modeling Scenarios.” Proceedings of the World Conference on Explainable Artificial
Intelligence (XAI), 2023, doi:10.1007/978-3-031-44064-9_11.'
short: 'M. Muschalik, F. Fumagalli, R. Jagtani, B. Hammer, E. Huellermeier, in:
Proceedings of the World Conference on Explainable Artificial Intelligence (XAI),
2023.'
date_created: 2023-11-10T14:17:17Z
date_updated: 2025-09-11T16:14:34Z
department:
- _id: '660'
doi: 10.1007/978-3-031-44064-9_11
language:
- iso: eng
project:
- _id: '126'
name: 'TRR 318 - C3: TRR 318 - Subproject C3'
- _id: '109'
name: 'TRR 318: TRR 318 - Erklärbarkeit konstruieren'
- _id: '117'
name: 'TRR 318 - C: TRR 318 - Project Area C'
publication: Proceedings of the World Conference on Explainable Artificial Intelligence
(xAI)
publication_identifier:
eisbn:
- 1865-0937
eissn:
- '9783031440649'
isbn:
- '9783031440632'
issn:
- 1865-0929
publication_status: published
status: public
title: 'iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios'
type: conference
user_id: '93420'
year: '2023'
...
---
_id: '48776'
author:
- first_name: Maximilian
full_name: Muschalik, Maximilian
last_name: Muschalik
- first_name: Fabian
full_name: Fumagalli, Fabian
id: '93420'
last_name: Fumagalli
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
- first_name: Eyke
full_name: Huellermeier, Eyke
id: '48129'
last_name: Huellermeier
citation:
ama: 'Muschalik M, Fumagalli F, Hammer B, Huellermeier E. iSAGE: An Incremental
Version of SAGE for Online Explanation on Data Streams. In: Machine Learning
and Knowledge Discovery in Databases: Research Track - European Conference (ECML
PKDD). Springer Nature Switzerland; 2023. doi:10.1007/978-3-031-43418-1_26'
apa: 'Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2023). iSAGE:
An Incremental Version of SAGE for Online Explanation on Data Streams. In Machine
Learning and Knowledge Discovery in Databases: Research Track - European Conference
(ECML PKDD). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43418-1_26'
bibtex: '@inbook{Muschalik_Fumagalli_Hammer_Huellermeier_2023, title={iSAGE: An
Incremental Version of SAGE for Online Explanation on Data Streams}, DOI={10.1007/978-3-031-43418-1_26},
booktitle={Machine Learning and Knowledge Discovery in Databases: Research Track
- European Conference (ECML PKDD)}, publisher={Springer Nature Switzerland}, author={Muschalik,
Maximilian and Fumagalli, Fabian and Hammer, Barbara and Huellermeier, Eyke},
year={2023} }'
chicago: 'Muschalik, Maximilian, Fabian Fumagalli, Barbara Hammer, and Eyke Huellermeier.
“ISAGE: An Incremental Version of SAGE for Online Explanation on Data Streams.”
In Machine Learning and Knowledge Discovery in Databases: Research Track -
European Conference (ECML PKDD). Springer Nature Switzerland, 2023. https://doi.org/10.1007/978-3-031-43418-1_26.'
ieee: 'M. Muschalik, F. Fumagalli, B. Hammer, and E. Huellermeier, “iSAGE: An Incremental
Version of SAGE for Online Explanation on Data Streams,” in Machine Learning
and Knowledge Discovery in Databases: Research Track - European Conference (ECML
PKDD), Springer Nature Switzerland, 2023.'
mla: 'Muschalik, Maximilian, et al. “ISAGE: An Incremental Version of SAGE for Online
Explanation on Data Streams.” Machine Learning and Knowledge Discovery in Databases:
Research Track - European Conference (ECML PKDD), Springer Nature Switzerland,
2023, doi:10.1007/978-3-031-43418-1_26.'
short: 'M. Muschalik, F. Fumagalli, B. Hammer, E. Huellermeier, in: Machine Learning
and Knowledge Discovery in Databases: Research Track - European Conference (ECML
PKDD), Springer Nature Switzerland, 2023.'
date_created: 2023-11-10T14:11:20Z
date_updated: 2025-09-11T16:27:26Z
department:
- _id: '660'
doi: 10.1007/978-3-031-43418-1_26
language:
- iso: eng
project:
- _id: '126'
name: 'TRR 318 - C3: TRR 318 - Subproject C3'
- _id: '117'
name: 'TRR 318 - C: TRR 318 - Project Area C'
- _id: '109'
name: 'TRR 318: TRR 318 - Erklärbarkeit konstruieren'
publication: 'Machine Learning and Knowledge Discovery in Databases: Research Track
- European Conference (ECML PKDD)'
publication_identifier:
eisbn:
- '9783031434181'
eissn:
- 1611-3349
isbn:
- '9783031434174'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature Switzerland
status: public
title: 'iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams'
type: book_chapter
user_id: '93420'
year: '2023'
...
---
_id: '48775'
author:
- first_name: Fabian
full_name: Fumagalli, Fabian
id: '93420'
last_name: Fumagalli
- first_name: Maximilian
full_name: Muschalik, Maximilian
last_name: Muschalik
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
citation:
ama: 'Fumagalli F, Muschalik M, Hüllermeier E, Hammer B. On Feature Removal for
Explainability in Dynamic Environments. In: Proceedings of the European Symposium
on Artificial Neural Networks (ESANN). ; 2023. doi:10.14428/ESANN/2023.ES2023-148'
apa: Fumagalli, F., Muschalik, M., Hüllermeier, E., & Hammer, B. (2023). On
Feature Removal for Explainability in Dynamic Environments. Proceedings of
the European Symposium on Artificial Neural Networks (ESANN). ESANN 2023 -
European Symposium on Artificial Neural Networks, Computational Intelligence and
Machine Learning, Bruges (Belgium) and online. https://doi.org/10.14428/ESANN/2023.ES2023-148
bibtex: '@inproceedings{Fumagalli_Muschalik_Hüllermeier_Hammer_2023, title={On Feature
Removal for Explainability in Dynamic Environments}, DOI={10.14428/ESANN/2023.ES2023-148},
booktitle={Proceedings of the European Symposium on Artificial Neural Networks
(ESANN)}, author={Fumagalli, Fabian and Muschalik, Maximilian and Hüllermeier,
Eyke and Hammer, Barbara}, year={2023} }'
chicago: Fumagalli, Fabian, Maximilian Muschalik, Eyke Hüllermeier, and Barbara
Hammer. “On Feature Removal for Explainability in Dynamic Environments.” In Proceedings
of the European Symposium on Artificial Neural Networks (ESANN), 2023. https://doi.org/10.14428/ESANN/2023.ES2023-148.
ieee: 'F. Fumagalli, M. Muschalik, E. Hüllermeier, and B. Hammer, “On Feature Removal
for Explainability in Dynamic Environments,” presented at the ESANN 2023 - European
Symposium on Artificial Neural Networks, Computational Intelligence and Machine
Learning, Bruges (Belgium) and online, 2023, doi: 10.14428/ESANN/2023.ES2023-148.'
mla: Fumagalli, Fabian, et al. “On Feature Removal for Explainability in Dynamic
Environments.” Proceedings of the European Symposium on Artificial Neural Networks
(ESANN), 2023, doi:10.14428/ESANN/2023.ES2023-148.
short: 'F. Fumagalli, M. Muschalik, E. Hüllermeier, B. Hammer, in: Proceedings of
the European Symposium on Artificial Neural Networks (ESANN), 2023.'
conference:
location: Bruges (Belgium) and online
name: ESANN 2023 - European Symposium on Artificial Neural Networks, Computational
Intelligence and Machine Learning
date_created: 2023-11-10T14:00:08Z
date_updated: 2025-09-11T16:26:21Z
department:
- _id: '660'
doi: 10.14428/ESANN/2023.ES2023-148
language:
- iso: eng
project:
- _id: '126'
name: 'TRR 318 - C3: TRR 318 - Subproject C3'
- _id: '117'
name: 'TRR 318 - C: TRR 318 - Project Area C'
- _id: '109'
name: 'TRR 318: TRR 318 - Erklärbarkeit konstruieren'
publication: Proceedings of the European Symposium on Artificial Neural Networks (ESANN)
publication_identifier:
unknown:
- ' 978-2-87587-088-9'
publication_status: published
status: public
title: On Feature Removal for Explainability in Dynamic Environments
type: conference
user_id: '93420'
year: '2023'
...
---
_id: '52230'
author:
- first_name: Fabian
full_name: Fumagalli, Fabian
id: '93420'
last_name: Fumagalli
- first_name: Maximilian
full_name: Muschalik, Maximilian
last_name: Muschalik
- first_name: Patrick
full_name: Kolpaczki, Patrick
last_name: Kolpaczki
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
citation:
ama: 'Fumagalli F, Muschalik M, Kolpaczki P, Hüllermeier E, Hammer B. SHAP-IQ: Unified
Approximation of any-order Shapley Interactions. In: Advances in Neural Information
Processing Systems (NeurIPS). Vol 36. ; 2023:11515--11551.'
apa: 'Fumagalli, F., Muschalik, M., Kolpaczki, P., Hüllermeier, E., & Hammer,
B. (2023). SHAP-IQ: Unified Approximation of any-order Shapley Interactions. Advances
in Neural Information Processing Systems (NeurIPS), 36, 11515--11551.'
bibtex: '@inproceedings{Fumagalli_Muschalik_Kolpaczki_Hüllermeier_Hammer_2023, title={SHAP-IQ:
Unified Approximation of any-order Shapley Interactions}, volume={36}, booktitle={Advances
in Neural Information Processing Systems (NeurIPS)}, author={Fumagalli, Fabian
and Muschalik, Maximilian and Kolpaczki, Patrick and Hüllermeier, Eyke and Hammer,
Barbara}, year={2023}, pages={11515--11551} }'
chicago: 'Fumagalli, Fabian, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier,
and Barbara Hammer. “SHAP-IQ: Unified Approximation of Any-Order Shapley Interactions.”
In Advances in Neural Information Processing Systems (NeurIPS), 36:11515--11551,
2023.'
ieee: 'F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier, and B. Hammer,
“SHAP-IQ: Unified Approximation of any-order Shapley Interactions,” in Advances
in Neural Information Processing Systems (NeurIPS), 2023, vol. 36, pp. 11515--11551.'
mla: 'Fumagalli, Fabian, et al. “SHAP-IQ: Unified Approximation of Any-Order Shapley
Interactions.” Advances in Neural Information Processing Systems (NeurIPS),
vol. 36, 2023, pp. 11515--11551.'
short: 'F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier, B. Hammer, in:
Advances in Neural Information Processing Systems (NeurIPS), 2023, pp. 11515--11551.'
date_created: 2024-03-01T14:15:31Z
date_updated: 2025-09-11T16:18:16Z
department:
- _id: '660'
intvolume: ' 36'
language:
- iso: eng
page: 11515--11551
project:
- _id: '126'
name: 'TRR 318 - C3: TRR 318 - Subproject C3'
- _id: '109'
name: 'TRR 318: TRR 318 - Erklärbarkeit konstruieren'
- _id: '117'
name: 'TRR 318 - C: TRR 318 - Project Area C'
publication: Advances in Neural Information Processing Systems (NeurIPS)
status: public
title: 'SHAP-IQ: Unified Approximation of any-order Shapley Interactions'
type: conference
user_id: '93420'
volume: 36
year: '2023'
...
---
_id: '51209'
author:
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Jaroslaw
full_name: Kornowicz, Jaroslaw
id: '44029'
last_name: Kornowicz
orcid: 0000-0002-5654-9911
- first_name: Stefan
full_name: Heid, Stefan
id: '39640'
last_name: Heid
orcid: 0000-0002-9461-7372
- first_name: Kirsten
full_name: Thommes, Kirsten
id: '72497'
last_name: Thommes
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Hanselle JM, Kornowicz J, Heid S, Thommes K, Hüllermeier E. Comparing Humans
and Algorithms in Feature Ranking: A Case-Study in the Medical Domain. In: Leyer
M, Wichmann J, eds. LWDA’23: Learning, Knowledge, Data, Analysis. . ; 2023.'
apa: 'Hanselle, J. M., Kornowicz, J., Heid, S., Thommes, K., & Hüllermeier,
E. (2023). Comparing Humans and Algorithms in Feature Ranking: A Case-Study in
the Medical Domain. In M. Leyer & J. Wichmann (Eds.), LWDA’23: Learning,
Knowledge, Data, Analysis. .'
bibtex: '@inproceedings{Hanselle_Kornowicz_Heid_Thommes_Hüllermeier_2023, title={Comparing
Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain},
booktitle={LWDA’23: Learning, Knowledge, Data, Analysis. }, author={Hanselle,
Jonas Manuel and Kornowicz, Jaroslaw and Heid, Stefan and Thommes, Kirsten and
Hüllermeier, Eyke}, editor={Leyer, M and Wichmann, J}, year={2023} }'
chicago: 'Hanselle, Jonas Manuel, Jaroslaw Kornowicz, Stefan Heid, Kirsten Thommes,
and Eyke Hüllermeier. “Comparing Humans and Algorithms in Feature Ranking: A Case-Study
in the Medical Domain.” In LWDA’23: Learning, Knowledge, Data, Analysis. ,
edited by M Leyer and J Wichmann, 2023.'
ieee: 'J. M. Hanselle, J. Kornowicz, S. Heid, K. Thommes, and E. Hüllermeier, “Comparing
Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain,”
in LWDA’23: Learning, Knowledge, Data, Analysis. , 2023.'
mla: 'Hanselle, Jonas Manuel, et al. “Comparing Humans and Algorithms in Feature
Ranking: A Case-Study in the Medical Domain.” LWDA’23: Learning, Knowledge,
Data, Analysis. , edited by M Leyer and J Wichmann, 2023.'
short: 'J.M. Hanselle, J. Kornowicz, S. Heid, K. Thommes, E. Hüllermeier, in: M.
Leyer, J. Wichmann (Eds.), LWDA’23: Learning, Knowledge, Data, Analysis. , 2023.'
date_created: 2024-02-07T09:24:37Z
date_updated: 2024-12-09T08:09:28Z
department:
- _id: '178'
- _id: '184'
editor:
- first_name: M
full_name: Leyer, M
last_name: Leyer
- first_name: J
full_name: Wichmann, J
last_name: Wichmann
language:
- iso: eng
main_file_link:
- url: https://ceur-ws.org/Vol-3630/LWDA2023-paper38.pdf
project:
- _id: '125'
name: 'TRR 318 - C2: TRR 318 - Subproject C2'
publication: 'LWDA’23: Learning, Knowledge, Data, Analysis. '
publication_identifier:
issn:
- 1613-0073
status: public
title: 'Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical
Domain'
type: conference
user_id: '72497'
year: '2023'
...
---
_id: '30868'
abstract:
- lang: eng
text: "Algorithm configuration (AC) is concerned with the automated search of the\r\nmost
suitable parameter configuration of a parametrized algorithm. There is\r\ncurrently
a wide variety of AC problem variants and methods proposed in the\r\nliterature.
Existing reviews do not take into account all derivatives of the AC\r\nproblem,
nor do they offer a complete classification scheme. To this end, we\r\nintroduce
taxonomies to describe the AC problem and features of configuration\r\nmethods,
respectively. We review existing AC literature within the lens of our\r\ntaxonomies,
outline relevant design choices of configuration approaches,\r\ncontrast methods
and problem variants against each other, and describe the\r\nstate of AC in industry.
Finally, our review provides researchers and\r\npractitioners with a look at future
research directions in the field of AC."
author:
- first_name: Elias
full_name: Schede, Elias
last_name: Schede
- first_name: Jasmin
full_name: Brandt, Jasmin
last_name: Brandt
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Viktor
full_name: Bengs, Viktor
id: '76599'
last_name: Bengs
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Kevin
full_name: Tierney, Kevin
last_name: Tierney
citation:
ama: Schede E, Brandt J, Tornede A, et al. A Survey of Methods for Automated Algorithm
Configuration. arXiv:220201651. Published online 2022.
apa: Schede, E., Brandt, J., Tornede, A., Wever, M. D., Bengs, V., Hüllermeier,
E., & Tierney, K. (2022). A Survey of Methods for Automated Algorithm Configuration.
In arXiv:2202.01651.
bibtex: '@article{Schede_Brandt_Tornede_Wever_Bengs_Hüllermeier_Tierney_2022, title={A
Survey of Methods for Automated Algorithm Configuration}, journal={arXiv:2202.01651},
author={Schede, Elias and Brandt, Jasmin and Tornede, Alexander and Wever, Marcel
Dominik and Bengs, Viktor and Hüllermeier, Eyke and Tierney, Kevin}, year={2022}
}'
chicago: Schede, Elias, Jasmin Brandt, Alexander Tornede, Marcel Dominik Wever,
Viktor Bengs, Eyke Hüllermeier, and Kevin Tierney. “A Survey of Methods for Automated
Algorithm Configuration.” ArXiv:2202.01651, 2022.
ieee: E. Schede et al., “A Survey of Methods for Automated Algorithm Configuration,”
arXiv:2202.01651. 2022.
mla: Schede, Elias, et al. “A Survey of Methods for Automated Algorithm Configuration.”
ArXiv:2202.01651, 2022.
short: E. Schede, J. Brandt, A. Tornede, M.D. Wever, V. Bengs, E. Hüllermeier, K.
Tierney, ArXiv:2202.01651 (2022).
date_created: 2022-04-12T12:00:08Z
date_updated: 2022-04-12T12:01:15Z
department:
- _id: '34'
- _id: '7'
- _id: '26'
external_id:
arxiv:
- '2202.01651'
language:
- iso: eng
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
name: 'SFB 901 - B2: SFB 901 - Subproject B2'
publication: arXiv:2202.01651
status: public
title: A Survey of Methods for Automated Algorithm Configuration
type: preprint
user_id: '38209'
year: '2022'
...
---
_id: '32311'
abstract:
- lang: eng
text: Testing is one of the most frequent means of quality assurance for software.
Property-based testing aims at generating test suites for checking code against
user-defined properties. Test input generation is, however, most often independent
of the property to be checked, and is instead based on random or user-defined
data generation.In this paper, we present property-driven unit testing of functions
with numerical inputs and outputs. Alike property-based testing, it allows users
to define the properties to be tested for. Contrary to property-based testing,
it also uses the property for a targeted generation of test inputs. Our approach
is a form of learning-based testing where we first of all learn a model of a given
black-box function using standard machine learning algorithms, and in a second
step use model and property for test input generation. This allows us to test
both predefined functions as well as machine learned regression models. Our experimental
evaluation shows that our property-driven approach is more effective than standard
property-based testing techniques.
author:
- first_name: Arnab
full_name: Sharma, Arnab
id: '67200'
last_name: Sharma
- first_name: Vitaly
full_name: Melnikov, Vitaly
id: '58747'
last_name: Melnikov
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Heike
full_name: Wehrheim, Heike
id: '573'
last_name: Wehrheim
citation:
ama: 'Sharma A, Melnikov V, Hüllermeier E, Wehrheim H. Property-Driven Testing of
Black-Box Functions. In: Proceedings of the 10th IEEE/ACM International Conference
on Formal Methods in Software Engineering (FormaliSE). IEEE; 2022:113-123.'
apa: Sharma, A., Melnikov, V., Hüllermeier, E., & Wehrheim, H. (2022). Property-Driven
Testing of Black-Box Functions. Proceedings of the 10th IEEE/ACM International
Conference on Formal Methods in Software Engineering (FormaliSE), 113–123.
bibtex: '@inproceedings{Sharma_Melnikov_Hüllermeier_Wehrheim_2022, title={Property-Driven
Testing of Black-Box Functions}, booktitle={Proceedings of the 10th IEEE/ACM International
Conference on Formal Methods in Software Engineering (FormaliSE)}, publisher={IEEE},
author={Sharma, Arnab and Melnikov, Vitaly and Hüllermeier, Eyke and Wehrheim,
Heike}, year={2022}, pages={113–123} }'
chicago: Sharma, Arnab, Vitaly Melnikov, Eyke Hüllermeier, and Heike Wehrheim. “Property-Driven
Testing of Black-Box Functions.” In Proceedings of the 10th IEEE/ACM International
Conference on Formal Methods in Software Engineering (FormaliSE), 113–23.
IEEE, 2022.
ieee: A. Sharma, V. Melnikov, E. Hüllermeier, and H. Wehrheim, “Property-Driven
Testing of Black-Box Functions,” in Proceedings of the 10th IEEE/ACM International
Conference on Formal Methods in Software Engineering (FormaliSE), 2022, pp.
113–123.
mla: Sharma, Arnab, et al. “Property-Driven Testing of Black-Box Functions.” Proceedings
of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering
(FormaliSE), IEEE, 2022, pp. 113–23.
short: 'A. Sharma, V. Melnikov, E. Hüllermeier, H. Wehrheim, in: Proceedings of
the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering
(FormaliSE), IEEE, 2022, pp. 113–123.'
date_created: 2022-07-01T11:18:03Z
date_updated: 2022-07-01T11:21:36Z
department:
- _id: '7'
language:
- iso: eng
page: 113-123
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '11'
name: 'SFB 901 - B3: SFB 901 - Subproject B3'
publication: Proceedings of the 10th IEEE/ACM International Conference on Formal Methods
in Software Engineering (FormaliSE)
publisher: IEEE
status: public
title: Property-Driven Testing of Black-Box Functions
type: conference
user_id: '477'
year: '2022'
...
---
_id: '34542'
author:
- first_name: Andrea
full_name: Campagner, Andrea
last_name: Campagner
- first_name: Julian
full_name: Lienen, Julian
id: '44040'
last_name: Lienen
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Davide
full_name: Ciucci, Davide
last_name: Ciucci
citation:
ama: 'Campagner A, Lienen J, Hüllermeier E, Ciucci D. Scikit-Weak: A Python Library
for Weakly Supervised Machine Learning. In: Lecture Notes in Computer Science.
Vol 13633. Springer; 2022:57-70.'
apa: 'Campagner, A., Lienen, J., Hüllermeier, E., & Ciucci, D. (2022). Scikit-Weak:
A Python Library for Weakly Supervised Machine Learning. Lecture Notes in Computer
Science, 13633, 57–70.'
bibtex: '@inproceedings{Campagner_Lienen_Hüllermeier_Ciucci_2022, title={Scikit-Weak:
A Python Library for Weakly Supervised Machine Learning}, volume={13633}, booktitle={Lecture
Notes in Computer Science}, publisher={Springer}, author={Campagner, Andrea and
Lienen, Julian and Hüllermeier, Eyke and Ciucci, Davide}, year={2022}, pages={57–70}
}'
chicago: 'Campagner, Andrea, Julian Lienen, Eyke Hüllermeier, and Davide Ciucci.
“Scikit-Weak: A Python Library for Weakly Supervised Machine Learning.” In Lecture
Notes in Computer Science, 13633:57–70. Springer, 2022.'
ieee: 'A. Campagner, J. Lienen, E. Hüllermeier, and D. Ciucci, “Scikit-Weak: A Python
Library for Weakly Supervised Machine Learning,” in Lecture Notes in Computer
Science, Suzhou, China, 2022, vol. 13633, pp. 57–70.'
mla: 'Campagner, Andrea, et al. “Scikit-Weak: A Python Library for Weakly Supervised
Machine Learning.” Lecture Notes in Computer Science, vol. 13633, Springer,
2022, pp. 57–70.'
short: 'A. Campagner, J. Lienen, E. Hüllermeier, D. Ciucci, in: Lecture Notes in
Computer Science, Springer, 2022, pp. 57–70.'
conference:
end_date: 2022-11-14
location: Suzhou, China
name: International Joint Conference on Rough Sets
start_date: 2022-11-11
date_created: 2022-12-19T09:34:35Z
date_updated: 2022-12-19T09:34:44Z
intvolume: ' 13633'
language:
- iso: eng
page: 57-70
publication: Lecture Notes in Computer Science
publisher: Springer
status: public
title: 'Scikit-Weak: A Python Library for Weakly Supervised Machine Learning'
type: conference
user_id: '44040'
volume: 13633
year: '2022'
...
---
_id: '31546'
abstract:
- lang: eng
text: In semi-supervised learning, the paradigm of self-training refers to the idea
of learning from pseudo-labels suggested by the learner itself. Across various
domains, corresponding methods have proven effective and achieve state-of-the-art
performance. However, pseudo-labels typically stem from ad-hoc heuristics, relying
on the quality of the predictions though without guaranteeing their validity.
One such method, so-called credal self-supervised learning, maintains pseudo-supervision
in the form of sets of (instead of single) probability distributions over labels,
thereby allowing for a flexible yet uncertainty-aware labeling. Again, however,
there is no justification beyond empirical effectiveness. To address this deficiency,
we make use of conformal prediction, an approach that comes with guarantees on
the validity of set-valued predictions. As a result, the construction of credal
sets of labels is supported by a rigorous theoretical foundation, leading to better
calibrated and less error-prone supervision for unlabeled data. Along with this,
we present effective algorithms for learning from credal self-supervision. An
empirical study demonstrates excellent calibration properties of the pseudo-supervision,
as well as the competitiveness of our method on several benchmark datasets.
author:
- first_name: Julian
full_name: Lienen, Julian
id: '44040'
last_name: Lienen
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Lienen J, Demir C, Hüllermeier E. Conformal Credal Self-Supervised Learning.
arXiv:220515239. Published online 2022.
apa: Lienen, J., Demir, C., & Hüllermeier, E. (2022). Conformal Credal Self-Supervised
Learning. In arXiv:2205.15239.
bibtex: '@article{Lienen_Demir_Hüllermeier_2022, title={Conformal Credal Self-Supervised
Learning}, journal={arXiv:2205.15239}, author={Lienen, Julian and Demir, Caglar
and Hüllermeier, Eyke}, year={2022} }'
chicago: Lienen, Julian, Caglar Demir, and Eyke Hüllermeier. “Conformal Credal Self-Supervised
Learning.” ArXiv:2205.15239, 2022.
ieee: J. Lienen, C. Demir, and E. Hüllermeier, “Conformal Credal Self-Supervised
Learning,” arXiv:2205.15239. 2022.
mla: Lienen, Julian, et al. “Conformal Credal Self-Supervised Learning.” ArXiv:2205.15239,
2022.
short: J. Lienen, C. Demir, E. Hüllermeier, ArXiv:2205.15239 (2022).
date_created: 2022-05-31T07:05:36Z
date_updated: 2022-05-31T07:05:54Z
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2205.15239
oa: '1'
publication: arXiv:2205.15239
status: public
title: Conformal Credal Self-Supervised Learning
type: preprint
user_id: '44040'
year: '2022'
...
---
_id: '30867'
abstract:
- lang: eng
text: "In online algorithm selection (OAS), instances of an algorithmic problem\r\nclass
are presented to an agent one after another, and the agent has to quickly\r\nselect
a presumably best algorithm from a fixed set of candidate algorithms.\r\nFor decision
problems such as satisfiability (SAT), quality typically refers to\r\nthe algorithm's
runtime. As the latter is known to exhibit a heavy-tail\r\ndistribution, an algorithm
is normally stopped when exceeding a predefined\r\nupper time limit. As a consequence,
machine learning methods used to optimize\r\nan algorithm selection strategy in
a data-driven manner need to deal with\r\nright-censored samples, a problem that
has received little attention in the\r\nliterature so far. In this work, we revisit
multi-armed bandit algorithms for\r\nOAS and discuss their capability of dealing
with the problem. Moreover, we\r\nadapt them towards runtime-oriented losses,
allowing for partially censored\r\ndata while keeping a space- and time-complexity
independent of the time\r\nhorizon. In an extensive experimental evaluation on
an adapted version of the\r\nASlib benchmark, we demonstrate that theoretically
well-founded methods based\r\non Thompson sampling perform specifically strong
and improve in comparison to\r\nexisting methods."
author:
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Viktor
full_name: Bengs, Viktor
id: '76599'
last_name: Bengs
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Tornede A, Bengs V, Hüllermeier E. Machine Learning for Online Algorithm Selection
under Censored Feedback. Proceedings of the 36th AAAI Conference on Artificial
Intelligence. Published online 2022.
apa: Tornede, A., Bengs, V., & Hüllermeier, E. (2022). Machine Learning for
Online Algorithm Selection under Censored Feedback. In Proceedings of the 36th
AAAI Conference on Artificial Intelligence. AAAI.
bibtex: '@article{Tornede_Bengs_Hüllermeier_2022, title={Machine Learning for Online
Algorithm Selection under Censored Feedback}, journal={Proceedings of the 36th
AAAI Conference on Artificial Intelligence}, publisher={AAAI}, author={Tornede,
Alexander and Bengs, Viktor and Hüllermeier, Eyke}, year={2022} }'
chicago: Tornede, Alexander, Viktor Bengs, and Eyke Hüllermeier. “Machine Learning
for Online Algorithm Selection under Censored Feedback.” Proceedings of the
36th AAAI Conference on Artificial Intelligence. AAAI, 2022.
ieee: A. Tornede, V. Bengs, and E. Hüllermeier, “Machine Learning for Online Algorithm
Selection under Censored Feedback,” Proceedings of the 36th AAAI Conference
on Artificial Intelligence. AAAI, 2022.
mla: Tornede, Alexander, et al. “Machine Learning for Online Algorithm Selection
under Censored Feedback.” Proceedings of the 36th AAAI Conference on Artificial
Intelligence, AAAI, 2022.
short: A. Tornede, V. Bengs, E. Hüllermeier, Proceedings of the 36th AAAI Conference
on Artificial Intelligence (2022).
date_created: 2022-04-12T11:58:56Z
date_updated: 2022-08-24T12:44:27Z
department:
- _id: '34'
- _id: '7'
- _id: '26'
external_id:
arxiv:
- '2109.06234'
language:
- iso: eng
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
name: 'SFB 901 - B2: SFB 901 - Subproject B2'
publication: Proceedings of the 36th AAAI Conference on Artificial Intelligence
publisher: AAAI
status: public
title: Machine Learning for Online Algorithm Selection under Censored Feedback
type: preprint
user_id: '38209'
year: '2022'
...
---
_id: '30865'
abstract:
- lang: eng
text: "The problem of selecting an algorithm that appears most suitable for a\r\nspecific
instance of an algorithmic problem class, such as the Boolean\r\nsatisfiability
problem, is called instance-specific algorithm selection. Over\r\nthe past decade,
the problem has received considerable attention, resulting in\r\na number of different
methods for algorithm selection. Although most of these\r\nmethods are based on
machine learning, surprisingly little work has been done\r\non meta learning,
that is, on taking advantage of the complementarity of\r\nexisting algorithm selection
methods in order to combine them into a single\r\nsuperior algorithm selector.
In this paper, we introduce the problem of meta\r\nalgorithm selection, which
essentially asks for the best way to combine a given\r\nset of algorithm selectors.
We present a general methodological framework for\r\nmeta algorithm selection
as well as several concrete learning methods as\r\ninstantiations of this framework,
essentially combining ideas of meta learning\r\nand ensemble learning. In an extensive
experimental evaluation, we demonstrate\r\nthat ensembles of algorithm selectors
can significantly outperform single\r\nalgorithm selectors and have the potential
to form the new state of the art in\r\nalgorithm selection."
author:
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Lukas
full_name: Gehring, Lukas
last_name: Gehring
- first_name: Tanja
full_name: Tornede, Tanja
id: '40795'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Tornede A, Gehring L, Tornede T, Wever MD, Hüllermeier E. Algorithm Selection
on a Meta Level. Machine Learning. Published online 2022.
apa: Tornede, A., Gehring, L., Tornede, T., Wever, M. D., & Hüllermeier, E.
(2022). Algorithm Selection on a Meta Level. In Machine Learning.
bibtex: '@article{Tornede_Gehring_Tornede_Wever_Hüllermeier_2022, title={Algorithm
Selection on a Meta Level}, journal={Machine Learning}, author={Tornede, Alexander
and Gehring, Lukas and Tornede, Tanja and Wever, Marcel Dominik and Hüllermeier,
Eyke}, year={2022} }'
chicago: Tornede, Alexander, Lukas Gehring, Tanja Tornede, Marcel Dominik Wever,
and Eyke Hüllermeier. “Algorithm Selection on a Meta Level.” Machine Learning,
2022.
ieee: A. Tornede, L. Gehring, T. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm
Selection on a Meta Level,” Machine Learning. 2022.
mla: Tornede, Alexander, et al. “Algorithm Selection on a Meta Level.” Machine
Learning, 2022.
short: A. Tornede, L. Gehring, T. Tornede, M.D. Wever, E. Hüllermeier, Machine Learning
(2022).
date_created: 2022-04-12T11:55:18Z
date_updated: 2022-08-24T12:45:39Z
department:
- _id: '34'
- _id: '7'
- _id: '26'
external_id:
arxiv:
- '2107.09414'
language:
- iso: eng
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
name: 'SFB 901 - B2: SFB 901 - Subproject B2'
publication: Machine Learning
status: public
title: Algorithm Selection on a Meta Level
type: preprint
user_id: '38209'
year: '2022'
...
---
_id: '33090'
abstract:
- lang: eng
text: 'AbstractHeated tool butt welding is a method
often used for joining thermoplastics, especially when the components are made
out of different materials. The quality of the connection between the components
crucially depends on a suitable choice of the parameters of the welding process,
such as heating time, temperature, and the precise way how the parts are then
welded. Moreover, when different materials are to be joined, the parameter values
need to be tailored to the specifics of the respective material. To this end,
in this paper, three approaches to tailor the parameter values to optimize the
quality of the connection are compared: a heuristic by Potente, statistical experimental
design, and Bayesian optimization. With the suitability for practice in mind,
a series of experiments are carried out with these approaches, and their capabilities
of proposing well-performing parameter values are investigated. As a result, Bayesian
optimization is found to yield peak performance, but the costs for optimization
are substantial. In contrast, the Potente heuristic does not require any experimentation
and recommends parameter values with competitive quality.'
author:
- first_name: Karina
full_name: Gevers, Karina
id: '83151'
last_name: Gevers
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Volker
full_name: Schöppner, Volker
id: '20530'
last_name: Schöppner
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Gevers K, Tornede A, Wever MD, Schöppner V, Hüllermeier E. A comparison of
heuristic, statistical, and machine learning methods for heated tool butt welding
of two different materials. Welding in the World. Published online 2022.
doi:10.1007/s40194-022-01339-9
apa: Gevers, K., Tornede, A., Wever, M. D., Schöppner, V., & Hüllermeier, E.
(2022). A comparison of heuristic, statistical, and machine learning methods for
heated tool butt welding of two different materials. Welding in the World.
https://doi.org/10.1007/s40194-022-01339-9
bibtex: '@article{Gevers_Tornede_Wever_Schöppner_Hüllermeier_2022, title={A comparison
of heuristic, statistical, and machine learning methods for heated tool butt welding
of two different materials}, DOI={10.1007/s40194-022-01339-9},
journal={Welding in the World}, publisher={Springer Science and Business Media
LLC}, author={Gevers, Karina and Tornede, Alexander and Wever, Marcel Dominik
and Schöppner, Volker and Hüllermeier, Eyke}, year={2022} }'
chicago: Gevers, Karina, Alexander Tornede, Marcel Dominik Wever, Volker Schöppner,
and Eyke Hüllermeier. “A Comparison of Heuristic, Statistical, and Machine Learning
Methods for Heated Tool Butt Welding of Two Different Materials.” Welding in
the World, 2022. https://doi.org/10.1007/s40194-022-01339-9.
ieee: 'K. Gevers, A. Tornede, M. D. Wever, V. Schöppner, and E. Hüllermeier, “A
comparison of heuristic, statistical, and machine learning methods for heated
tool butt welding of two different materials,” Welding in the World, 2022,
doi: 10.1007/s40194-022-01339-9.'
mla: Gevers, Karina, et al. “A Comparison of Heuristic, Statistical, and Machine
Learning Methods for Heated Tool Butt Welding of Two Different Materials.” Welding
in the World, Springer Science and Business Media LLC, 2022, doi:10.1007/s40194-022-01339-9.
short: K. Gevers, A. Tornede, M.D. Wever, V. Schöppner, E. Hüllermeier, Welding
in the World (2022).
date_created: 2022-08-24T12:51:07Z
date_updated: 2022-08-24T12:52:06Z
doi: 10.1007/s40194-022-01339-9
keyword:
- Metals and Alloys
- Mechanical Engineering
- Mechanics of Materials
language:
- iso: eng
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
name: 'SFB 901 - B2: SFB 901 - Subproject B2'
publication: Welding in the World
publication_identifier:
issn:
- 0043-2288
- 1878-6669
publication_status: published
publisher: Springer Science and Business Media LLC
status: public
title: A comparison of heuristic, statistical, and machine learning methods for heated
tool butt welding of two different materials
type: journal_article
user_id: '38209'
year: '2022'
...
---
_id: '36227'
author:
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Volker
full_name: Lohweg, Volker
last_name: Lohweg
- first_name: Alexander
full_name: Schneider, Alexander
last_name: Schneider
- first_name: Wolfram
full_name: Schenck, Wolfram
last_name: Schenck
- first_name: Ulrike
full_name: Kuhl, Ulrike
last_name: Kuhl
- first_name: Marco
full_name: Braun, Marco
last_name: Braun
- first_name: Anton
full_name: Pfeifer, Anton
last_name: Pfeifer
- first_name: Christoph-Alexander
full_name: Holst, Christoph-Alexander
last_name: Holst
- first_name: Malte
full_name: Schmidt, Malte
last_name: Schmidt
- first_name: Gunnar
full_name: Schomaker, Gunnar
last_name: Schomaker
- first_name: Tanja
full_name: Tornede, Tanja
id: '40795'
last_name: Tornede
citation:
ama: 'Hammer B, Hüllermeier E, Lohweg V, et al. Schlussbericht ITS.ML: Intelligente
Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben
zur automatisierten Analyse von Daten mittels Maschinellen Lernens.; 2022.
doi:10.4119/unibi/2965622'
apa: 'Hammer, B., Hüllermeier, E., Lohweg, V., Schneider, A., Schenck, W., Kuhl,
U., Braun, M., Pfeifer, A., Holst, C.-A., Schmidt, M., Schomaker, G., & Tornede,
T. (2022). Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten
Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse
von Daten mittels Maschinellen Lernens. https://doi.org/10.4119/unibi/2965622'
bibtex: '@book{Hammer_Hüllermeier_Lohweg_Schneider_Schenck_Kuhl_Braun_Pfeifer_Holst_Schmidt_et
al._2022, title={Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten
Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse
von Daten mittels Maschinellen Lernens}, DOI={10.4119/unibi/2965622},
author={Hammer, Barbara and Hüllermeier, Eyke and Lohweg, Volker and Schneider,
Alexander and Schenck, Wolfram and Kuhl, Ulrike and Braun, Marco and Pfeifer,
Anton and Holst, Christoph-Alexander and Schmidt, Malte and et al.}, year={2022}
}'
chicago: 'Hammer, Barbara, Eyke Hüllermeier, Volker Lohweg, Alexander Schneider,
Wolfram Schenck, Ulrike Kuhl, Marco Braun, et al. Schlussbericht ITS.ML: Intelligente
Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben
zur automatisierten Analyse von Daten mittels Maschinellen Lernens, 2022.
https://doi.org/10.4119/unibi/2965622.'
ieee: 'B. Hammer et al., Schlussbericht ITS.ML: Intelligente Technische
Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben
zur automatisierten Analyse von Daten mittels Maschinellen Lernens. 2022.'
mla: 'Hammer, Barbara, et al. Schlussbericht ITS.ML: Intelligente Technische
Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben
zur automatisierten Analyse von Daten mittels Maschinellen Lernens. 2022,
doi:10.4119/unibi/2965622.'
short: 'B. Hammer, E. Hüllermeier, V. Lohweg, A. Schneider, W. Schenck, U. Kuhl,
M. Braun, A. Pfeifer, C.-A. Holst, M. Schmidt, G. Schomaker, T. Tornede, Schlussbericht
ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles
Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen
Lernens, 2022.'
date_created: 2023-01-11T15:00:00Z
date_updated: 2023-01-11T15:20:40Z
ddc:
- '004'
department:
- _id: '34'
- _id: '7'
- _id: '534'
doi: 10.4119/unibi/2965622
has_accepted_license: '1'
language:
- iso: ger
status: public
title: 'Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation
durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten
mittels Maschinellen Lernens'
type: report
user_id: '40795'
year: '2022'
...
---
_id: '48780'
abstract:
- lang: eng
text: Explainable Artificial Intelligence (XAI) has mainly focused on static learning
tasks so far. In this paper, we consider XAI in the context of online learning
in dynamic environments, such as learning from real-time data streams, where models
are learned incrementally and continuously adapted over the course of time. More
specifically, we motivate the problem of explaining model change, i.e. explaining
the difference between models before and after adaptation, instead of the models
themselves. In this regard, we provide the first efficient model-agnostic approach
to dynamically detecting, quantifying, and explaining significant model changes.
Our approach is based on an adaptation of the well-known Permutation Feature Importance
(PFI) measure. It includes two hyperparameters that control the sensitivity and
directly influence explanation frequency, so that a human user can adjust the
method to individual requirements and application needs. We assess and validate
our method’s efficacy on illustrative synthetic data streams with three popular
model classes.
author:
- first_name: Maximilian
full_name: Muschalik, Maximilian
last_name: Muschalik
- first_name: Fabian
full_name: Fumagalli, Fabian
id: '93420'
last_name: Fumagalli
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
- first_name: Eyke
full_name: Huellermeier, Eyke
id: '48129'
last_name: Huellermeier
citation:
ama: Muschalik M, Fumagalli F, Hammer B, Huellermeier E. Agnostic Explanation of
Model Change based on Feature Importance. KI - Künstliche Intelligenz.
2022;36(3-4):211-224. doi:10.1007/s13218-022-00766-6
apa: Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2022). Agnostic
Explanation of Model Change based on Feature Importance. KI - Künstliche Intelligenz,
36(3–4), 211–224. https://doi.org/10.1007/s13218-022-00766-6
bibtex: '@article{Muschalik_Fumagalli_Hammer_Huellermeier_2022, title={Agnostic
Explanation of Model Change based on Feature Importance}, volume={36}, DOI={10.1007/s13218-022-00766-6},
number={3–4}, journal={KI - Künstliche Intelligenz}, publisher={Springer Science
and Business Media LLC}, author={Muschalik, Maximilian and Fumagalli, Fabian and
Hammer, Barbara and Huellermeier, Eyke}, year={2022}, pages={211–224} }'
chicago: 'Muschalik, Maximilian, Fabian Fumagalli, Barbara Hammer, and Eyke Huellermeier.
“Agnostic Explanation of Model Change Based on Feature Importance.” KI - Künstliche
Intelligenz 36, no. 3–4 (2022): 211–24. https://doi.org/10.1007/s13218-022-00766-6.'
ieee: 'M. Muschalik, F. Fumagalli, B. Hammer, and E. Huellermeier, “Agnostic Explanation
of Model Change based on Feature Importance,” KI - Künstliche Intelligenz,
vol. 36, no. 3–4, pp. 211–224, 2022, doi: 10.1007/s13218-022-00766-6.'
mla: Muschalik, Maximilian, et al. “Agnostic Explanation of Model Change Based on
Feature Importance.” KI - Künstliche Intelligenz, vol. 36, no. 3–4, Springer
Science and Business Media LLC, 2022, pp. 211–24, doi:10.1007/s13218-022-00766-6.
short: M. Muschalik, F. Fumagalli, B. Hammer, E. Huellermeier, KI - Künstliche Intelligenz
36 (2022) 211–224.
date_created: 2023-11-10T14:21:06Z
date_updated: 2025-01-16T16:19:35Z
department:
- _id: '660'
doi: 10.1007/s13218-022-00766-6
intvolume: ' 36'
issue: 3-4
keyword:
- Artificial Intelligence
language:
- iso: eng
page: 211-224
project:
- _id: '126'
name: 'TRR 318 - C3: TRR 318 - Subproject C3'
- _id: '117'
name: 'TRR 318 - C: TRR 318 - Project Area C'
- _id: '109'
grant_number: '438445824'
name: 'TRR 318: TRR 318 - Erklärbarkeit konstruieren'
publication: KI - Künstliche Intelligenz
publication_identifier:
issn:
- 0933-1875
- 1610-1987
publication_status: published
publisher: Springer Science and Business Media LLC
status: public
title: Agnostic Explanation of Model Change based on Feature Importance
type: journal_article
user_id: '93420'
volume: 36
year: '2022'
...
---
_id: '24143'
author:
- first_name: Jan Peter
full_name: Drees, Jan Peter
last_name: Drees
- first_name: Pritha
full_name: Gupta, Pritha
id: '54803'
last_name: Gupta
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Tibor
full_name: Jager, Tibor
last_name: Jager
- first_name: Alexander
full_name: Konze, Alexander
last_name: Konze
- first_name: Claudia
full_name: Priesterjahn, Claudia
last_name: Priesterjahn
- first_name: Arunselvan
full_name: Ramaswamy, Arunselvan
id: '66937'
last_name: Ramaswamy
orcid: https://orcid.org/ 0000-0001-7547-8111
- first_name: Juraj
full_name: Somorovsky, Juraj
id: '83504'
last_name: Somorovsky
orcid: 0000-0002-3593-7720
citation:
ama: 'Drees JP, Gupta P, Hüllermeier E, et al. Automated Detection of Side Channels
in Cryptographic Protocols: DROWN the ROBOTs! 14th ACM Workshop on Artificial
Intelligence and Security. Published online 2021.'
apa: 'Drees, J. P., Gupta, P., Hüllermeier, E., Jager, T., Konze, A., Priesterjahn,
C., Ramaswamy, A., & Somorovsky, J. (2021). Automated Detection of Side Channels
in Cryptographic Protocols: DROWN the ROBOTs! 14th ACM Workshop on Artificial
Intelligence and Security.'
bibtex: '@article{Drees_Gupta_Hüllermeier_Jager_Konze_Priesterjahn_Ramaswamy_Somorovsky_2021,
title={Automated Detection of Side Channels in Cryptographic Protocols: DROWN
the ROBOTs!}, journal={14th ACM Workshop on Artificial Intelligence and Security},
author={Drees, Jan Peter and Gupta, Pritha and Hüllermeier, Eyke and Jager, Tibor
and Konze, Alexander and Priesterjahn, Claudia and Ramaswamy, Arunselvan and Somorovsky,
Juraj}, year={2021} }'
chicago: 'Drees, Jan Peter, Pritha Gupta, Eyke Hüllermeier, Tibor Jager, Alexander
Konze, Claudia Priesterjahn, Arunselvan Ramaswamy, and Juraj Somorovsky. “Automated
Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs!” 14th
ACM Workshop on Artificial Intelligence and Security, 2021.'
ieee: 'J. P. Drees et al., “Automated Detection of Side Channels in Cryptographic
Protocols: DROWN the ROBOTs!,” 14th ACM Workshop on Artificial Intelligence
and Security, 2021.'
mla: 'Drees, Jan Peter, et al. “Automated Detection of Side Channels in Cryptographic
Protocols: DROWN the ROBOTs!” 14th ACM Workshop on Artificial Intelligence
and Security, 2021.'
short: J.P. Drees, P. Gupta, E. Hüllermeier, T. Jager, A. Konze, C. Priesterjahn,
A. Ramaswamy, J. Somorovsky, 14th ACM Workshop on Artificial Intelligence and
Security (2021).
date_created: 2021-09-10T09:56:27Z
date_updated: 2022-01-06T06:56:08Z
department:
- _id: '632'
language:
- iso: eng
publication: 14th ACM Workshop on Artificial Intelligence and Security
status: public
title: 'Automated Detection of Side Channels in Cryptographic Protocols: DROWN the
ROBOTs!'
type: journal_article
user_id: '83504'
year: '2021'
...
---
_id: '24148'
author:
- first_name: Arunselvan
full_name: Ramaswamy, Arunselvan
id: '66937'
last_name: Ramaswamy
orcid: https://orcid.org/ 0000-0001-7547-8111
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Ramaswamy A, Hüllermeier E. Deep Q-Learning: Theoretical Insights from an
Asymptotic Analysis. IEEE Transactions on Artificial Intelligence (to appear).
Published online 2021.'
apa: 'Ramaswamy, A., & Hüllermeier, E. (2021). Deep Q-Learning: Theoretical
Insights from an Asymptotic Analysis. IEEE Transactions on Artificial Intelligence
(to Appear).'
bibtex: '@article{Ramaswamy_Hüllermeier_2021, title={Deep Q-Learning: Theoretical
Insights from an Asymptotic Analysis}, journal={IEEE Transactions on Artificial
Intelligence (to appear)}, author={Ramaswamy, Arunselvan and Hüllermeier, Eyke},
year={2021} }'
chicago: 'Ramaswamy, Arunselvan, and Eyke Hüllermeier. “Deep Q-Learning: Theoretical
Insights from an Asymptotic Analysis.” IEEE Transactions on Artificial Intelligence
(to Appear), 2021.'
ieee: 'A. Ramaswamy and E. Hüllermeier, “Deep Q-Learning: Theoretical Insights from
an Asymptotic Analysis,” IEEE Transactions on Artificial Intelligence (to appear),
2021.'
mla: 'Ramaswamy, Arunselvan, and Eyke Hüllermeier. “Deep Q-Learning: Theoretical
Insights from an Asymptotic Analysis.” IEEE Transactions on Artificial Intelligence
(to Appear), 2021.'
short: A. Ramaswamy, E. Hüllermeier, IEEE Transactions on Artificial Intelligence
(to Appear) (2021).
date_created: 2021-09-10T10:03:25Z
date_updated: 2022-01-06T06:56:08Z
language:
- iso: eng
publication: IEEE Transactions on Artificial Intelligence (to appear)
status: public
title: 'Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis'
type: journal_article
user_id: '66937'
year: '2021'
...
---
_id: '21004'
abstract:
- lang: eng
text: 'Automated machine learning (AutoML) supports the algorithmic construction
and data-specific customization of machine learning pipelines, including the selection,
combination, and parametrization of machine learning algorithms as main constituents.
Generally speaking, AutoML approaches comprise two major components: a search
space model and an optimizer for traversing the space. Recent approaches have
shown impressive results in the realm of supervised learning, most notably (single-label)
classification (SLC). Moreover, first attempts at extending these approaches towards
multi-label classification (MLC) have been made. While the space of candidate
pipelines is already huge in SLC, the complexity of the search space is raised
to an even higher power in MLC. One may wonder, therefore, whether and to what
extent optimizers established for SLC can scale to this increased complexity,
and how they compare to each other. This paper makes the following contributions:
First, we survey existing approaches to AutoML for MLC. Second, we augment these
approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking
framework that supports a fair and systematic comparison. Fourth, we conduct an
extensive experimental study, evaluating the methods on a suite of MLC problems.
We find a grammar-based best-first search to compare favorably to other optimizers.'
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification:
Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and
Machine Intelligence. Published online 2021:1-1. doi:10.1109/tpami.2021.3051276'
apa: 'Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (2021). AutoML
for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions
on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2021.3051276'
bibtex: '@article{Wever_Tornede_Mohr_Hüllermeier_2021, title={AutoML for Multi-Label
Classification: Overview and Empirical Evaluation}, DOI={10.1109/tpami.2021.3051276},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, author={Wever,
Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke},
year={2021}, pages={1–1} }'
chicago: 'Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier.
“AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” IEEE
Transactions on Pattern Analysis and Machine Intelligence, 2021, 1–1. https://doi.org/10.1109/tpami.2021.3051276.'
ieee: 'M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label
Classification: Overview and Empirical Evaluation,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, pp. 1–1, 2021, doi: 10.1109/tpami.2021.3051276.'
mla: 'Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview
and Empirical Evaluation.” IEEE Transactions on Pattern Analysis and Machine
Intelligence, 2021, pp. 1–1, doi:10.1109/tpami.2021.3051276.'
short: M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern
Analysis and Machine Intelligence (2021) 1–1.
date_created: 2021-01-16T14:48:13Z
date_updated: 2022-01-06T06:54:42Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
doi: 10.1109/tpami.2021.3051276
keyword:
- Automated Machine Learning
- Multi Label Classification
- Hierarchical Planning
- Bayesian Optimization
language:
- iso: eng
page: 1-1
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_identifier:
issn:
- 0162-8828
- 2160-9292
- 1939-3539
publication_status: published
status: public
title: 'AutoML for Multi-Label Classification: Overview and Empirical Evaluation'
type: journal_article
user_id: '5786'
year: '2021'
...
---
_id: '21092'
abstract:
- lang: eng
text: "Automated Machine Learning (AutoML) seeks to automatically find so-called
machine learning pipelines that maximize the prediction performance when being
used to train a model on a given dataset. One of the main and yet open challenges
in AutoML is an effective use of computational resources: An AutoML process involves
the evaluation of many candidate pipelines, which are costly but often ineffective
because they are canceled due to a timeout.\r\nIn this paper, we present an approach
to predict the runtime of two-step machine learning pipelines with up to one pre-processor,
which can be used to anticipate whether or not a pipeline will time out. Separate
runtime models are trained offline for each algorithm that may be used in a pipeline,
and an overall prediction is derived from these models. We empirically show that
the approach increases successful evaluations made by an AutoML tool while preserving
or even improving on the previously best solutions."
author:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Mohr F, Wever MD, Tornede A, Hüllermeier E. Predicting Machine Learning Pipeline
Runtimes in the Context of Automated Machine Learning. IEEE Transactions on
Pattern Analysis and Machine Intelligence.
apa: Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (n.d.). Predicting
Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.
IEEE Transactions on Pattern Analysis and Machine Intelligence.
bibtex: '@article{Mohr_Wever_Tornede_Hüllermeier, title={Predicting Machine Learning
Pipeline Runtimes in the Context of Automated Machine Learning}, journal={IEEE
Transactions on Pattern Analysis and Machine Intelligence}, publisher={IEEE},
author={Mohr, Felix and Wever, Marcel Dominik and Tornede, Alexander and Hüllermeier,
Eyke} }'
chicago: Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier.
“Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine
Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence,
n.d.
ieee: F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “Predicting Machine
Learning Pipeline Runtimes in the Context of Automated Machine Learning,” IEEE
Transactions on Pattern Analysis and Machine Intelligence.
mla: Mohr, Felix, et al. “Predicting Machine Learning Pipeline Runtimes in the Context
of Automated Machine Learning.” IEEE Transactions on Pattern Analysis and Machine
Intelligence, IEEE.
short: F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, IEEE Transactions on Pattern
Analysis and Machine Intelligence (n.d.).
date_created: 2021-01-27T13:45:52Z
date_updated: 2022-01-06T06:54:45Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_status: accepted
publisher: IEEE
status: public
title: Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine
Learning
type: journal_article
user_id: '5786'
year: '2021'
...
---
_id: '21570'
author:
- first_name: Tanja
full_name: Tornede, Tanja
id: '40795'
last_name: Tornede
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede T, Tornede A, Wever MD, Hüllermeier E. Coevolution of Remaining Useful
Lifetime Estimation Pipelines for Automated Predictive Maintenance. In: Proceedings
of the Genetic and Evolutionary Computation Conference. ; 2021.'
apa: Tornede, T., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Coevolution
of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.
Proceedings of the Genetic and Evolutionary Computation Conference. Genetic
and Evolutionary Computation Conference.
bibtex: '@inproceedings{Tornede_Tornede_Wever_Hüllermeier_2021, title={Coevolution
of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
author={Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier,
Eyke}, year={2021} }'
chicago: Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier.
“Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive
Maintenance.” In Proceedings of the Genetic and Evolutionary Computation Conference,
2021.
ieee: T. Tornede, A. Tornede, M. D. Wever, and E. Hüllermeier, “Coevolution of Remaining
Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance,” presented
at the Genetic and Evolutionary Computation Conference, 2021.
mla: Tornede, Tanja, et al. “Coevolution of Remaining Useful Lifetime Estimation
Pipelines for Automated Predictive Maintenance.” Proceedings of the Genetic
and Evolutionary Computation Conference, 2021.
short: 'T. Tornede, A. Tornede, M.D. Wever, E. Hüllermeier, in: Proceedings of the
Genetic and Evolutionary Computation Conference, 2021.'
conference:
end_date: 2021-07-14
name: Genetic and Evolutionary Computation Conference
start_date: 2021-07-10
date_created: 2021-03-26T09:14:19Z
date_updated: 2022-01-06T06:55:06Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proceedings of the Genetic and Evolutionary Computation Conference
status: public
title: Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated
Predictive Maintenance
type: conference
user_id: '5786'
year: '2021'
...